Comparar métodos

	Modelo EGARCH Bayesiano	Modelo ARCH (Autoregressive Conditional Heteroskedasticity)
Área	Econometria	Econometria
Família	Regression model	Regression model
Ano de origem	1991 (EGARCH); 2000s (Bayesian estimation)	1982
Autor original	Nelson (1991) for EGARCH; Bayesian inference via MCMC developed from early 2000s	Robert F. Engle
Tipo	Volatility model with Bayesian inference	Conditional volatility model
Fonte seminal	Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica, 59(2), 347–370.	Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50(4), 987–1007.
Outros nomes	Bayesian EGARCH model, Bayesian Exponential GARCH, EGARCH with Bayesian estimation, B-EGARCH	ARCH, autoregressive conditional heteroskedasticity, Engle ARCH, conditional variance model
Relacionados	6	6
Resumo	The Bayesian EGARCH model combines Nelson's (1991) Exponential GARCH specification — which models the log of conditional variance and captures the leverage effect — with Bayesian posterior inference via Markov Chain Monte Carlo (MCMC). This allows full uncertainty quantification of all volatility parameters, including the asymmetry coefficient, without requiring large-sample normality of the estimates.	The ARCH model, introduced by Robert Engle in 1982, captures time-varying volatility in financial and macroeconomic time series. It models the conditional variance of today's error as a function of past squared errors, explaining why volatile periods cluster together — a phenomenon known as volatility clustering.